Marketing data science attempts to answer three main questions:
1. How much does it cost to acquire a customer on a given channel?
2. How much do I earn from an acquired customer over their lifetime?
3. What is the causal impact of my marketing campaigns?

While seemingly straight-forward, robust estimation of these quantities on noisy, non-stationary and highly structured data is quite tricky. Moreover, while these questions are intimately related, they are often answered separately.

In this talk I will present two new open-source packages that make up a powerful and state-of-the-art marketing analytics toolbox. Specifically, PyMC-Marketing is a new library built on top of the popular Bayesian modeling library PyMC. PyMC-Marketing allows robust estimation of customer acquisition costs (via media mix modeling) as well as customer lifetime value.
In addition, I will show how we can estimate the effectiveness of marketing campaigns using a new Bayesian causal inference package called CausalPy. The talk will be applied with a real-world case-study and many code examples. Special emphasis will be placed on the interplay between these tools and how they can be combined.

Together, these tools demonstrated provide a powerful open-source suite to solve today's biggest marketing analytics challenges.